Can we isolate this issue to misbehavior of getfullargspec instead of tf.function?

I ask because as far as tf.function is concerned nothing breaks if we just wrap the partial call into a lambda *args, **kwds: partial_call(*args, **kwds) so we should probably just do that at intake time if the user passes a functools.partial function.

I am not actually sure that this is a misbehavior of getfullargspec.

Currently we are using ArgSpec to be able to bind arguments to (partial or normal) function later. We want to represent partial function fully with ArgSpec, but this is not possible with Python2.x: it is not possible to have arguments with defaults before arguments without defaults.

I would consider this more an integration issue. For example, do we really need to represent partial function using getfullargspec on the inner function (the one with more arguments)? Could we somehow forget about the inner partial and work with the outer only? I haven't tried it myself.

@vbardiovskyg I think you're working on this, right?

Since we are now depending on partial to do the argument binding, this becomes infeasible (i.e. we don't want to provide more functionality than partial already does).

Are you satisfied with the resolution of your issue?
Yes
No

@vbardiovskyg Please see comments in the following code (TF 2.2.0rc2). Why does the first case succeed and second fail? Is this a bug? Thanks.

import tensorflow as tf
import functools

def f(x, y):
    return x + y

bind_x = functools.partial(f, x=1)
bind_y = functools.partial(f, y=1)

dataset = tf.data.Dataset.range(1)

print(next(iter(dataset.map(bind_y))))  # This works
print(next(iter(dataset.map(bind_x))))  # TypeError: tf__f() got multiple values for argument 'x'

Hi @mmilosav,

this is due to how partial works. See the following snippet using partial outside of TensorFlow context:

def f(x,y):
  print(x+y)

bind_x = functools.partial(f, x=1)

bind_x(3)  # TypeError: f() got multiple values for argument 'x'